BiotModel

实现RBF神经网络，用MATLAB的M语言开发的(RBF neural network implementation with MATLAB M-language development)

利用最小二乘法拟合直线和曲线，绘制出对比图形；简单绘制多种三维图形的代码(least-square fiting and draw graph)

基于煤耗特性曲线的遗传算法1、运行主程序main，可根据需求修改相关参数，进行迭代； 2、每次修改数据或参数可修改储存的文件名称，以保存迭代数据； 3、运行Result输入要导出的储存文件的名称可得到相应的适应度函数曲线，适应度函数最小值和三个变量取值； 4、将上述三个变量值输入到huatu.m中，即可得到拟合的曲线图。(Genetic Algorithms Based on Coal Consumption Characteristic Curve)

用于解三对角方程的追赶法源代码，MATLAB编写，高斯列主元解线性方程组代码(Solution of tridiagonal equations for the pursuit method source code, MATLAB prepared)

PAPR Simulation using PTS based on Muller and Huber paper.

MPC5606的 DMA开发 负责吧数据从变量搬运出去(development DMA )

voc detection result. it is very useful to have it if you want to do human detection and pose estimation

首先给出了谐波小波时频剖面图检测含噪声信号的微弱奇异成分的方法 然后运用谐波小波时频剖面图方 法分析了碰摩故障仿真信号 最后利用该方法对多个实际汽轮机组动静碰摩故障的真实振动信号进行了分析研 究。结果表明, 谐波小波能够得到其它信号分析方法无法得到的特征, 有效识别机组的碰摩故障。(Harmonic wavelet time􀀁 frequency profile plot is introduced firstly in practice to identify weak singularity in a signal with noise clearly. With this method, emulational signal and vibration data of the rubbing of the large practical turbo-generator units are analyzed successfully, which prove that the method is effectively extract the rubb ing signal feature.)

The basic snake segmentation (2d and 3d). A snake is an active (moving) contour, in which the points are attracted by edges and other boundaries. To keep the contour smooth, an membrame and thin plate energy is used as regularization.

1、工具箱：LS_SVMlab Classification_LS_SVMlab.m - 多类分类 Regression_LS_SVMlab.m - 函数拟合 2、工具箱：OSU_SVM3.00 Classification_OSU_SVM.m - 多类分类 3、工具箱：stprtoolsvm Classification_stprtool.m - 多类分类 4、工具箱：SVM_SteveGunn Classification_SVM_SteveGunn.m - 二类分类 Regression_SVM_SteveGunn.m - 函数拟合 (a toolbox : LS_SVMlab Classification_LS_SVMlab.m-over classification Regression_LS_SVMlab.m-function fitting 2, toolbox : OSU_SVM3.00 Classification_OSU_SVM.m-over classification 3, toolbox : stprtool svm Classification_stprtool.m-over category Categories 4, toolbox : SVM_SteveGunn Classification_SVM_SteveGun n.m-II classification Regression_SVM_SteveGunn.m-fitting function)